Natural assistant interaction

ABSTRACT

Systems and processes for operating a virtual assistant to provide natural assistant interaction are provided. In accordance with one or more examples, a method includes, at an electronic device with one or more processors and memory: receiving a first audio stream including one or more utterances; determining whether the first audio stream includes a lexical trigger; generating one or more candidate text representations of the one or more utterances; determining whether at least one candidate text representation of the one or more candidate text representations is to be disregarded by the virtual assistant. If at least one candidate text representation is to be disregarded, one or more candidate intents are generated based on candidate text representations of the one or more candidate text representations other than the to be disregarded at least one candidate text representation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/019,331, filed Jun. 26, 2018, entitled “NATURAL ASSISTANTINTERACTION,” which claims priority to U.S. Provisional Application Ser.No. 62/648,084, entitled “NATURAL ASSISTANT INTERACTION,” filed on Mar.26, 2018, the content of which are hereby incorporated by reference intheir entirety.

FIELD

This relates generally to virtual assistants and, more specifically, toproviding natural language interaction by virtual assistants.

BACKGROUND

Virtual assistants (or digital assistants or intelligent automatedassistants) can provide a beneficial human-machine interface. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The virtual assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

Virtual assistants can be activated upon receiving a trigger phrase suchas “Hey Siri.” Upon activation, virtual assistants can receive andprocess user's speech input. For example, a user's speech input mayinclude a leading trigger phrase to activate the virtual assistantfollowed by a request for information (e.g., “Hey Siri, how is theweather today?”). Leading every speech input with a trigger phrase(e.g., “Hey Siri”), however, can be inconvenient and quickly becomecumbersome. It also does not represent a natural way of communication.For example, when a first user talks to a second user, the first usertypically would not lead every sentence with the name of the seconduser. Thus, requiring the user to lead each speech input with a triggerphrase does not represent a natural way of communication and is lessefficient.

SUMMARY

Systems and processes for providing natural language interaction by avirtual assistant are provided.

In accordance with one or more examples, a method includes, at anelectronic device with one or more processors, memory, and a microphone:receiving, via the microphone, a first audio stream including one ormore utterances and determining whether the first audio stream includesa lexical trigger. In accordance with a determination that the firstaudio stream includes the lexical trigger, the method further includesgenerating one or more candidate text representations of the one or moreutterances and determining whether at least one candidate textrepresentation of the one or more candidate text representations is tobe disregarded by the virtual assistant. In accordance with adetermination that at least one candidate text representation is to bedisregarded by the virtual assistant, the method further includesgenerating one or more candidate intents based on candidate textrepresentations of the one or more candidate text representations otherthan the to be disregarded at least one candidate text representation.The method further includes determining whether the one or morecandidate intents include at least one actionable intent. In accordancewith a determination that the one or more candidate intents include atleast one actionable intent, the method further includes executing theat least one actionable intent and outputting a result of the executionof the at least one actionable intent.

Example non-transitory computer-readable media are disclosed herein. Anexample non-transitory computer-readable storage medium stores one ormore programs. The one or more programs comprise instructions, whichwhen executed by one or more processors of an electronic device, causethe electronic device to receive, via a microphone, a first audio streamincluding one or more utterances; determine whether the first audiostream includes a lexical trigger; in accordance with a determinationthat the first audio stream includes the lexical trigger, generate oneor more candidate text representations of the one or more utterances;determine whether at least one candidate text representation of the oneor more candidate text representations is to be disregarded by thevirtual assistant; in accordance with a determination that at least onecandidate text representation is to be disregarded by the virtualassistant, generate one or more candidate intents based on candidatetext representations of the one or more candidate text representationsother than the to be disregarded at least one candidate textrepresentation; determine whether the one or more candidate intentsinclude at least one actionable intent; in accordance with adetermination that the one or more candidate intents include at leastone actionable intent, execute the at least one actionable intent; andoutput a result of the execution of the at least one actionable intent.

Example electronic devices are disclosed herein. An example electronicdevice comprises one or more processors; a memory; and one or moreprograms, where the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for, receiving, via the microphone, afirst audio stream including one or more utterances; determining whetherthe first audio stream includes a lexical trigger; in accordance with adetermination that the first audio stream includes the lexical trigger,generating one or more candidate text representations of the one or moreutterances; determining whether at least one candidate textrepresentation of the one or more candidate text representations is tobe disregarded by the virtual assistant; in accordance with adetermination that at least one candidate text representation is to bedisregarded by the virtual assistant, generating one or more candidateintents based on candidate text representations of the one or morecandidate text representations other than the to be disregarded at leastone candidate text representation; determining whether the one or morecandidate intents include at least one actionable intent; in accordancewith a determination that the one or more candidate intents include atleast one actionable intent, executing the at least one actionableintent; outputting a result of the execution of the at least oneactionable intent.

An example electronic device comprises means for receiving, via themicrophone, a first audio stream including one or more utterances; meansfor determining whether the first audio stream includes a lexicaltrigger; in accordance with a determination that the first audio streamincludes the lexical trigger, means for generating one or more candidatetext representations of the one or more utterances; means fordetermining whether at least one candidate text representation of theone or more candidate text representations is to be disregarded by thevirtual assistant; in accordance with a determination that at least onecandidate text representation is to be disregarded by the virtualassistant, means for generating one or more candidate intents based oncandidate text representations of the one or more candidate textrepresentations other than the to be disregarded at least one candidatetext representation; means for determining whether the one or morecandidate intents include at least one actionable intent; in accordancewith a determination that the one or more candidate intents include atleast one actionable intent, means for executing the at least oneactionable intent; and means for outputting a result of the execution ofthe at least one actionable intent.

Current techniques facilitating speech-based human-machine interactiontypically require using a trigger phrase at the beginning portion of anutterance from the user. As described above, this requirement can causethe human-machine interaction to become cumbersome and make thehuman-machine user interface less natural and efficient. Varioustechniques for providing natural language interaction described in thisapplication eliminates or reduces the need of this requirement to leadevery user utterance with a trigger phrase. Instead, a trigger word orphrase can be placed in any portion of an audio stream that may includeone or more user utterances. Moreover, the techniques described in thisapplication do not require using a trigger phrase that includes aplurality of words (e.g., “Hey Siri”). A single word (e.g., “Siri”) canbe used to indicate that the audio stream including the user utterancesis directed to the virtual assistant. This enables a more natural way ofcommunication.

Furthermore, various techniques for facilitating speech-basedhuman-machine interaction described in this application enhance theoperability of the device and makes the user-device interface moreefficient (e.g., by not requiring leading every user utterance with atrigger phrase) which, additionally, reduces power usage and improvesbattery life of the device by enabling the user to use the device morequickly and efficiently.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIG. 8 illustrates a block diagram of an exemplary virtual assistant forproviding natural language interaction.

FIG. 9 illustrates an exemplary user interface for providing naturallanguage interaction by a virtual assistant.

FIG. 10 illustrates a block diagram of an exemplary virtual assistantfor providing natural language interaction using context information.

FIG. 11A illustrates an exemplary user interface for providing naturallanguage interaction by a virtual assistant using context informationassociated with a usage pattern.

FIG. 11B illustrates an exemplary user interface for providing naturallanguage interaction by a virtual assistant using context informationassociated with sensory data.

FIGS. 12A-12D illustrate exemplary user interfaces for providing naturallanguage interaction by a virtual assistant using context informationassociated with executing a previously determined actionable intent.

FIGS. 13A-13B illustrate exemplary user interfaces for providing naturallanguage interaction by a virtual assistant using context informationassociated with a relation of user utterances or audio streams.

FIGS. 14A-14D illustrate exemplary user interfaces for selecting a taskfrom a plurality of tasks using context information.

FIGS. 15A-15G illustrate a process for providing natural languageinteraction by a virtual assistant, according to various embodiments.

DETAILED DESCRIPTION

In the following description of embodiments, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

Various techniques for facilitating a more natural human-machineinteraction are described. The techniques include reducing oreliminating the need for leading a user utterance with a trigger phraseand using a false-trigger mitigator to improve accuracy associated withdetermining whether a user utterance is directed to a virtual assistant.The techniques also include performing candidate intent evaluationwithout actual execution (e.g., making a dry run), thereby determiningwhether a candidate intent is actionable. This determination avoidswasting processing power, user confusion, and thus improves operationalefficiency of the device.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B.) A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 is a non-portablemultifunctional device. In particular, user device 104 is a desktopcomputer, a game console, a television, or a television set-top box. Insome examples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and return relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), 6,570,557(Westerman et al.), and/or 6,677,932 (Westerman), and/or U.S. PatentPublication 2002/0015024A1, each of which is hereby incorporated byreference in its entirety. However, touch screen 212 displays visualoutput from device 200, whereas touch-sensitive touchpads do not providevisual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 229, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of: weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive displayscreen 604, hereafter touch screen 604. Alternatively, or in addition totouch screen 604, device 600 has a display and a touch-sensitivesurface. As with devices 200 and 400, in some embodiments, touch screen604 (or the touch-sensitive surface) has one or more intensity sensorsfor detecting intensity of contacts (e.g., touches) being applied. Theone or more intensity sensors of touch screen 604 (or thetouch-sensitive surface) provide output data that represents theintensity of touches. The user interface of device 600 responds totouches based on their intensity, meaning that touches of differentintensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 200, 400, and/or 600 (FIGS. 2A, 4, and 6A-B).For example, an image (e.g., icon), a button, and text (e.g., hyperlink)each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (I/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and I/O communication interfacesdescribed with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-B,respectively. In some examples, digital assistant system 700 representsthe server portion of a digital assistant implementation, and caninteract with the user through a client-side portion residing on a userdevice (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 alsoincludes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis module 740. Each of thesemodules has access to one or more of the following systems or data andmodels of the digital assistant module 726, or a subset or supersetthereof: ontology 760, vocabulary index 744, user data 748, task flowmodels 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The oneor more ASR systems 758 can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system 758 includes a front-end speech pre-processor. The front-endspeech pre-processor extracts representative features from the speechinput. For example, the front-end speech pre-processor performs aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system 758 includes one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and implements one or more speech recognition engines. Examplesof speech recognition models include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines areused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input is processed at least partially by athird-party service or on the user's device (e.g., device 104, 200, 400,or 600) to produce the recognition result. Once STT processing module730 produces recognition results containing a text string (e.g., words,or sequence of words, or sequence of tokens), the recognition result ispassed to natural language processing module 732 for intent deduction.In some examples, STT processing module 730 produces multiple candidatetext representations of the speech input. Each candidate textrepresentation is a sequence of words or tokens corresponding to thespeech input. In some examples, each candidate text representation isassociated with a speech recognition confidence score. Based on thespeech recognition confidence scores, STT processing module 730 ranksthe candidate text representations and provides the n-best (e.g., nhighest ranked) candidate text representation(s) to natural languageprocessing module 732 for intent deduction, where n is a predeterminedinteger greater than zero. For example, in one example, only the highestranked (n=1) candidate text representation is passed to natural languageprocessing module 732 for intent deduction. In another example, the fivehighest ranked (n=5) candidate text representations are passed tonatural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the n-best candidate text representation(s)(“word sequence(s)” or “token sequence(s)”) generated by STT processingmodule 730, and attempts to associate each of the candidate textrepresentations with one or more “actionable intents” recognized by thedigital assistant. In some embodiments, as described in more detailbelow, STT processing module 730 attempts to associate each of thecandidate text representation with one or more “candidate intents” usinga false trigger mitigator (FTM). The FTM provides the candidate intentsto a candidate intent evaluator (CIE), which evaluates whether thecandidate intents include one or more “actionable intent.” An“actionable intent” (or “user intent”) represents a task that can beperformed by the digital assistant, and can have an associated task flowimplemented in task flow models 754. The associated task flow is aseries of programmed actions and steps that the digital assistant takesin order to perform the task. The scope of a digital assistant'scapabilities is dependent on the number and variety of task flows thathave been implemented and stored in task flow models 754, or in otherwords, on the number and variety of “actionable intents” that thedigital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe candidate text representations received from STT processing module730. The contextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the candidate textrepresentations (e.g., text string(s) or token sequence(s)) from STTprocessing module 730, and for each candidate representation, determineswhat nodes are implicated by the words in the candidate textrepresentation. In some examples, if a word or phrase in the candidatetext representation is found to be associated with one or more nodes inontology 760 (via vocabulary index 744), the word or phrase “triggers”or “activates” those nodes. Based on the quantity and/or relativeimportance of the activated nodes, natural language processing module732 selects one of the actionable intents as the task that the userintended the digital assistant to perform. In some examples, the domainthat has the most “triggered” nodes is selected. In some examples, thedomain having the highest confidence value (e.g., based on the relativeimportance of its various triggered nodes) is selected. In someexamples, the domain is selected based on a combination of the numberand the importance of the triggered nodes. In some examples, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

It should be recognized that in some examples, natural languageprocessing module 732 is implemented using one or more machine learningmechanisms (e.g., neural networks). In particular, the one or moremachine learning mechanisms are configured to receive a candidate textrepresentation and contextual information associated with the candidatetext representation. Based on the candidate text representation and theassociated contextual information, the one or more machine learningmechanism are configured to determine intent confidence scores over aset of candidate actionable intents. Natural language processing module732 can select one or more candidate actionable intents from the set ofcandidate actionable intents based on the determined intent confidencescores. In some examples, an ontology (e.g., ontology 760) is also usedto select the one or more candidate actionable intents from the set ofcandidate actionable intents.

Other details of searching an ontology based on a token string isdescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as (Cuisine), (Time), (Date), (Party Size), and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as (Party Size) and (Date) is notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a (location) parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 identifiesmultiple candidate actionable intents for each candidate textrepresentation received from STT processing module 730. Further, in someexamples, a respective structured query (partial or complete) isgenerated for each identified candidate actionable intent. Naturallanguage processing module 732 determines an intent confidence score foreach candidate actionable intent and ranks the candidate actionableintents based on the intent confidence scores. In some examples, naturallanguage processing module 732 passes the generated structured query (orqueries), including any completed parameters, to task flow processingmodule 736 (“task flow processor”). In some examples, the structuredquery (or queries) for the m-best (e.g., m highest ranked) candidateactionable intents are provided to task flow processing module 736,where m is a predetermined integer greater than zero. In some examples,the structured query (or queries) for the m-best candidate actionableintents are provided to task flow processing module 736 with thecorresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidateactionable intents determined from multiple candidate textrepresentations of a speech input are described in U.S. Utilityapplication Ser. No. 14/298,725 for “System and Method for InferringUser Intent From Speech Inputs,” filed Jun. 6, 2014, the entiredisclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structuredquery (or queries) from natural language processing module 732, completethe structured query, if necessary, and perform the actions required to“complete” the user's ultimate request. In some examples, the variousprocedures necessary to complete these tasks are provided in task flowmodels 754. In some examples, task flow models 754 include proceduresfor obtaining additional information from the user and task flows forperforming actions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, taskflow processing module 736 performs the steps of: (1) logging onto aserver of the ABC Café or a restaurant reservation system such asOPENTABLE®, (2) entering the date, time, and party size information in aform on the website, (3) submitting the form, and (4) making a calendarentry for the reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generaterequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and send thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis module 740 (e.g., speechsynthesizer) where it can be processed to synthesize the dialogueresponse in speech form. In yet other examples, the generated responseis data content relevant to satisfying a user request in the speechinput.

In examples where task flow processing module 736 receives multiplestructured queries from natural language processing module 732, taskflow processing module 736 initially processes the first structuredquery of the received structured queries to attempt to complete thefirst structured query and/or execute one or more tasks or actionsrepresented by the first structured query. In some examples, the firststructured query corresponds to the highest ranked actionable intent. Inother examples, the first structured query is selected from the receivedstructured queries based on a combination of the corresponding speechrecognition confidence scores and the corresponding intent confidencescores. In some examples, if task flow processing module 736 encountersan error during processing of the first structured query (e.g., due toan inability to determine a necessary parameter), the task flowprocessing module 736 can proceed to select and process a secondstructured query of the received structured queries that corresponds toa lower ranked actionable intent. The second structured query isselected, for example, based on the speech recognition confidence scoreof the corresponding candidate text representation, the intentconfidence score of the corresponding candidate actionable intent, amissing necessary parameter in the first structured query, or anycombination thereof.

Speech synthesis module 740 is configured to synthesize speech outputsfor presentation to the user. Speech synthesis module 740 synthesizesspeech outputs based on text provided by the digital assistant. Forexample, the generated dialogue response is in the form of a textstring. Speech synthesis module 740 converts the text string to anaudible speech output. Speech synthesis module 740 uses any appropriatespeech synthesis technique in order to generate speech outputs fromtext, including, but not limited, to concatenative synthesis, unitselection synthesis, diphone synthesis, domain-specific synthesis,formant synthesis, articulatory synthesis, hidden Markov model (HMM)based synthesis, and sinewave synthesis. In some examples, speechsynthesis module 740 is configured to synthesize individual words basedon phonemic strings corresponding to the words. For example, a phonemicstring is associated with a word in the generated dialogue response. Thephonemic string is stored in metadata associated with the word. Speechsynthesis model 740 is configured to directly process the phonemicstring in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesismodule 740, speech synthesis is performed on a remote device (e.g., theserver system 108), and the synthesized speech is sent to the userdevice for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Exemplary Architecture and Functionality of a Virtual Assistant

FIG. 8 illustrates a block diagram of a virtual assistant 800 forproviding natural language interaction. In some examples, virtualassistant 800 (e.g., digital assistant system 700) can be implemented bya user device according to various embodiments. In some embodiments, theuser device, a server (e.g., server 108), or a combination thereof, canimplement virtual assistant 800. The user device can be implementedusing, for example, device 104, 200, 400, 900, 1120, 1220, 1320, or 1420as illustrated in FIGS. 1, 2A-2B, 4, 9, 11A-11B, 12A-12D, 13A-13B, and14A-14D. In some embodiments, as illustrated in FIG. 8, virtualassistant 800 includes the following sub-modules, or a subset orsuperset thereof: an input module 810, a natural language engine 820, afalse trigger mitigator (FTM) 840, a candidate intent evaluator (CIE)860, and a task execution module 880. In some embodiments, virtualassistant 800 can be implemented using digital assistant module 726 ofdigital assistant system 700 shown in FIG. 7B. For example, virtualassistant 800 can include one or more modules, models, applications,vocabularies, and user data similar to those of digital assistant module726. With reference to FIGS. 7B and 8, as one example, input module 810can be, for example, a sub-module or a variation of Input/Outputprocessing module 728. Natural language engine 820 can include, forexample, one or more of STT processing module 730, phonetic alphabetconversion module 731, vocabulary 744, user data 748, and/or naturallanguage processing module 760. Virtual assistant 800 can also includemodules, models, applications, vocabularies, and user data that are notincluded in digital assistant module 726. For example, false triggermitigator 840 as shown in FIG. 8 may not be included in digitalassistant module 726.

With reference to FIG. 8, in some embodiments, input module 810 receivesone or more audio streams 802. An audio stream can include one or moreutterances. In some embodiments, an utterance in an audio stream caninclude a word, a phrase that includes a plurality of words, and/or oneor more sentences. As an example illustrated in FIG. 9, an audio stream912 may include one or more user utterances as sentences such as “It'sdark outside. Turn on the light, Siri.”

With reference back to FIG. 8, input module 810 can be activated orremain active (e.g., for a pre-configured period of time) to receive oneor more audio streams 802. For example, a voice activity detector 814 ofinput module 810 can detect the presence or absence of human voice(e.g., based on the amplitude and/or the frequency spectrum of the inputsignals received at voice activity detector 814). In some embodiments,input module 810 can include a buffer 812 (e.g., a ring buffer)configured to store one or more received audio streams (e.g., store 10seconds of audio streams).

In some embodiments, using the received audio streams stored in buffer812, voice activity detector 814 can determine whether an audio streamincludes a lexical trigger. A lexical trigger can include a single wordor a plurality of words. For example, a lexical trigger can be “HeySiri,” “Hey Assistant,” “Siri,” “Assistant,” or the like. In someembodiments, upon receiving a lexical trigger by input module 810 (e.g.,the voice activity detector 814), at least a portion of virtualassistant 800 (e.g., NLE 820) is activated. In some embodiments, atleast a portion of virtual assistant 800 (e.g., input module 810, NLE820, FTM 840) can remain active or remain active for a pre-configuredperiod of time. For example, input module 810 can remain active toreceive audio streams 802 for 30 seconds. In the embodiments where atleast a portion of virtual assistant 800 remain active for at least aper-configured period of time, a lexical trigger may not cause a portionof virtual assistant 800 to become activated. Instead, a lexical triggermay indicate that at least a portion of an audio stream is directed tothe virtual assistant.

Using the above example shown in FIG. 9, the utterance “Turn on thelight, Siri” includes a single-word lexical trigger “Siri,” whichindicates that the utterance is directed to the virtual assistantoperating on device 900. In some embodiments, a lexical trigger can bepositioned at the beginning portion of an utterance in an audio stream(e.g., “Siri, what is the stock price?”). In some embodiments, a lexicaltrigger can be positioned at any portion other than the beginningportion of the utterance (e.g., “Turn on the light, Siri.”). Allowingthe lexical trigger to be positioned at any portion other than thebeginning portion of the utterance enables a more natural human-machineinteraction between a user and the virtual assistant, rather thanrequiring the user to lead every utterance directed to the virtualassistant with the lexical trigger. This enhances the user experience,reduces power consumption, and improves system efficiency.

With reference back to FIG. 8, as described above, input module 810receives one or more audio streams 802. Virtual assistant 800 (e.g.,using VAD 814 of input module 810) can determine the beginningpoint/ending point and/or the duration of a particular audio stream ofthe one or more audio streams 802. Based on such determination, virtualassistant 800 can determine whether a lexical trigger is included withinthe particular audio stream.

As one example, to detect the beginning point of a particular audiostream, virtual assistant 800 detects, via a microphone (not shown inFIG. 8), an absence of voice activity before receiving the particularaudio stream (e.g., detecting a silence or a pause between adjacentutterances). Virtual assistant 800 can further determine whether theabsence of voice activity before receiving the particular audio streamexceeds a first threshold period of time (e.g., 3 seconds). If theabsence of voice activity exceeds the first threshold period of time,virtual assistant 800 determines the beginning point of the particularaudio stream. For instance, if the first threshold period of time is 3seconds and if virtual assistant 800 determines that before receivingthe first audio stream, there is a 5-second silence or that input module810 does not receive any audio input for the past 5 seconds, virtualassistant 800 determines that the period of absence of voice activityexceeds the first threshold period of time. As a result, the beginningpoint of the particular audio stream can be determined.

In some embodiments, to detect an end point of a particular audiostream, virtual assistant 800 can detect, via a microphone (not shown inFIG. 8), an absence of voice activity after receiving one or moreutterances of the particular audio stream (e.g., detecting a silence ora pause between adjacent utterances). Virtual assistant 800 can furtherdetermine whether the absence of voice activity after receiving theparticular audio stream exceeds a second threshold period of time (e.g.,3 seconds). If the period of absence of voice activity exceeds thesecond threshold period of time, virtual assistant 800 determines theend point of the first audio stream. For instance, if the secondthreshold period of time is 3 seconds and if virtual assistant 800determines that after receiving the particular audio stream, there is a5-second silence or that input module 810 does not receive any furtheraudio input for the next 5 seconds, virtual assistant 800 can determinethat the period of absence of voice activity exceeds the secondthreshold period of time. As a result, the end point of the particularaudio stream can be determined.

As an example shown in FIG. 9, a particular audio stream 912 may includea first utterance (e.g., “It's dark outside.”) and a second utterance(e.g., “Turn on the light, Siri.”). The virtual assistant operating ondevice 900 (e.g., virtual assistant 800) can determine that beforereceiving the first utterance of audio stream 912, no audio input isreceived for at least 3 seconds; and that after receiving the secondutterance audio stream 912, no audio input is received for at least 3seconds. The virtual assistant may further determine that between thefirst utterance and the second utterance, there is only a short periodof absence of voice activity (e.g., a short pause of 0.5 second). As aresult, the virtual assistant can determine the beginning point/endpoint and/or the duration of audio stream 912.

In some embodiments, with reference to FIG. 8, an end point of an audiostream can be determined based on a pre-configured duration that inputmodule 810 is configured to receive an audio stream. For example, if oneor more user utterances in an audio stream are directed to a virtualassistant (e.g., to obtain information or to instruct the virtualassistant to perform a task), the user utterances typically may not lastfor more than 30 seconds. Accordingly, the pre-configured duration canbe set to be, for example, 30 seconds. As a result, any user utterancesreceived within 30 seconds following the detected beginning point of theparticular audio stream can be determined to be utterances of a sameaudio stream. Accordingly, to determine an end point of a particularaudio stream, in some embodiments, virtual assistant 800 (e.g., usinginput module 810) can detect the beginning point of an audio stream,obtain a pre-configured duration for an audio stream (e.g., 30 seconds),and determine the end point of the audio stream based on thepre-configured duration and the beginning point (e.g., the end point is30 seconds after the beginning point).

With reference to FIG. 8, in some embodiments, an end point of an audiostream can be determined based on the capacity of buffer 812. Buffer 812can store the received one or more audio streams as an audio file. Thestorage size of the audio file buffer 812 depends on the capacity ofbuffer 812 (e.g., in the range of megabytes). As an example ofdetermining the end point of an audio stream based on the capacity ofbuffer 812, virtual assistant 800 (e.g., using input module 810) candetermine a size of an audio file representing a received audio streamincluding one or more utterances and compare the size of the audio filewith a capacity of buffer 812. In some embodiments, if the size of theaudio file reaches the capacity of buffer 812 (e.g., substantially equalto the capacity of buffer 812), virtual assistant 800 can determine thatthe utterances included in the audio file represent an entire audiostream. Based on this determination result, virtual assistant 800 candetermine the end point of the audio stream.

In some embodiments, virtual assistant 800 can also estimate alikelihood that a particular absence of voice activity before or afterreceiving a particular audio stream corresponds to a beginning point oran end point of the particular audio stream, respectively. As describedabove, virtual assistant 800 can detect an absence of voice activitybefore or after receiving a speech input. In some examples, an automaticspeech recognition system (e.g., system 758 shown in FIG. 7A) of virtualassistant 800 processes the speech input and produces a recognitionresult. Based on the recognition result (e.g., text of the speech input)and one or more language models (e.g., models used by the ASR system 758as described above), virtual assistant 800 estimates a likelihood that aparticular absence of voice activity corresponds to a beginning point oran end point of the particular audio stream. In the above example ofaudio stream 912 (e.g., “Its' dark outside. Turn on the light, Siri.”),based on the recognition results of an ASR system and one or morelanguage models, virtual assistant 800 may estimate the likelihood thatan absence of voice activity (e.g., 3 seconds of silence) afterreceiving audio stream 912 corresponds to an end point of audio stream912. Based on the estimated likelihood, virtual assistant 800 can detectthe end point of audio stream 912 with an increased or improvedconfidence (e.g., based on comparison of the estimated likelihood to athreshold).

Upon detecting the end point and the beginning point of a particularaudio stream, virtual assistant 800 can determine the duration of theparticular audio stream. In some embodiments, based on the duration ofthe particular audio stream, virtual assistant 800 can determine whethera lexical trigger is included in the particular audio stream. In someembodiments, virtual assistant 800 does not determine the duration ofthe particular audio stream, but can determine whether a lexical triggeris included in the particular audio stream using the detected end pointand the detected beginning point of the particular audio stream.

As described above, FIG. 9 illustrates an audio stream 912 that includesone or more utterances (e.g., “It's dark outside. Turn on the light,Siri.”) and virtual assistant 800 determines that a lexical trigger(e.g., “Siri”) is included in audio stream 912. As shown in FIG. 9, theone or more utterances of audio stream 912 may include at least oneutterance that is not directed to virtual assistant 800. For example,the first utterance “It's dark outside” may be a comment and is not anutterance directed to the virtual assistant operating on device 900(e.g., virtual assistant 800). The virtual assistant may thus notperform a task or take an action based on such an utterance. Asdescribed in detail below, using various techniques, a virtual assistantcan determine that an utterance is not directed to it and thus is to bedisregarded. Such utterances can include, for example, user's comments,an utterance that is directed from one user to another, or the like. Theability for a virtual assistant to determine whether an utterance isdirected to it enhances a device's operational efficiency, because thevirtual assistant can disregard any utterances that are not directed toit while eliminating or reducing the requirements for leading everyutterance directed to the virtual assistant with a trigger word orphrase.

With reference to FIG. 8, in some embodiments, in accordance with adetermination that a particular audio stream of one or more audiostreams 802 includes a lexical trigger (e.g., “Siri”), input module 810generates one or more speech results 816. Speech results 816 can includeaudio representations (e.g., phonetic representations) of the one ormore utterances included in a particular audio stream. Input module 810further provide speech results 816 to natural language engine 820, whichcan include one or more modules such as speech-to-text (STT) processingmodule 730 and/or natural language process module 732 (shown in FIG.7B), or a variation thereof. Based on speech results 816, naturallanguage engine 820 can generate candidate text representations 822representing the one or more utterances in the particular audio stream.As described above, each candidate text representation can be a sequenceof words or tokens corresponding to the utterances in the particularaudio stream. Using audio stream 912 shown in FIG. 9 as an example, anatural language engine of the virtual assistant operating on device 900can perform speech-to-text conversion of each of the utterances of audiostream 912 and generate candidate text representations including a firstcandidate text representation (e.g., “It's dark outside.”) and a secondcandidate text representation (e.g., “Turn on the light, Siri.”).

In some embodiments, natural language engine 820 can further determineconfidence levels corresponding to the one or more candidate textrepresentations 822. For example, as described above, each candidatetext representation can be associated with a speech recognitionconfidence score. And natural language engine 820 can rank (e.g., usingSTT processing module 730) candidate text representations 822 andprovide the n-best (e.g., n highest ranked) candidate textrepresentation(s) for candidate intent generation or derivation.

As described above, natural language engine 820 can include, forexample, one or more of STT processing module 730, phonetic alphabetconversion module 731, vocabulary 744, user data 748, and/or naturallanguage processing module 760. In some embodiments, using naturallanguage processing module 760, natural language engine 820 can furtherinterpret the candidate text representations to derive pre-mitigationintents and optionally confidence levels associated with thepre-mitigation intents. For example, natural language engine 820 canoptionally rank confidence levels associated with the pre-mitigationintents and provide the n-best pre-mitigation intents for candidateintent generation or derivation.

With reference to FIG. 8, a false trigger mitigator (FTM) 840 of virtualassistant 800 can determine whether at least one candidate textrepresentation of the one or more candidate text representations 822 (orranked candidate text representations) is to be disregarded by virtualassistant 800. FTM 840 can be implemented using, for example, asub-module, or a variation, of digital assistant module 726 shown inFIG. 7B. For example, FTM 840 can include a natural language processing732, or a variation thereof, to derive one or more candidate intents842. In some embodiments, FTM 840 can include a decision tree such as asimple decision tree or a boosted decision tree. As described above, insome embodiments, at least one utterance of the one or more utterancesin a particular audio stream may not be directed to virtual assistant800 and thus can be disregarded by virtual assistant 800.

FIG. 9 illustrates one example of determining whether at least onecandidate text representation is to be disregarded. As shown in FIG. 9and described above, based on the utterances of user 910, a naturallanguage engine (e.g., NLE 820) of the virtual assistant operating ondevice 900 can generate, for example, two candidate text representationssuch as “It's dark outside.” and “Turn on the light, Siri.” In someembodiments, an FTM (e.g., FTM 840) of the virtual assistant determines,for each of the two candidate text representations, whether thecandidate text representation includes a lexical trigger. For example,the FTM determines that the first candidate text representation (e.g.,“It's dark outside.”) does not include a lexical trigger, but the secondcandidate text representation (e.g., “Turn on the light, Siri.”)includes a lexical trigger (e.g., “Siri”).

With reference to FIG. 8, in some embodiments, if FTM 840 determinesthat a candidate text representation includes a lexical trigger, FTM 840determines that the corresponding user utterance is directed to virtualassistant 800 and therefore the particular candidate text representationis not to be disregarded. The particular candidate text representationwould thus be further processed as described in more detail below. As anexample shown in FIG. 9, an FTM of the virtual assistant operating ondevice 900 determines that the second candidate text representation(e.g., “Turn on the light, Siri.”) is not to be disregarded because itincludes a single-word lexical trigger “Siri.”

With reference to FIG. 8, in some embodiments, if FTM 840 determinesthat a candidate text representation does not include a lexical trigger,FTM 840 can estimate a likelihood that the utterance corresponding tothe particular candidate text representation is not directed to virtualassistant 800. Using the example shown in FIG. 9, as described above, anFTM of the virtual assistant operating on device 900 determines that thefirst candidate text representation (e.g., “It's dark outside.”) doesnot include a lexical trigger. Further, using a decision tree, the FTMof the virtual assistant can estimate a likelihood that the utterancecorresponding to the first candidate text representation is not directedto the virtual assistant. For example, the FTM can determine that thepre-mitigation intent corresponding to the first candidate textrepresentation (e.g., “It's dark outside.”) is not or likely notassociated with a domain or a candidate intent recognized by the virtualassistant. As a result, the FTM can estimate the likelihood that theutterance corresponding to the first candidate text representation(e.g., “It's dark outside.”) is not directed to the virtual assistant,and determine whether the estimated likelihood satisfy a thresholdcondition. If so, the FTM determines that the utterance is not directedto the virtual assistant. As a result, the first candidate textrepresentation (e.g., “It's dark outside.”) can be disregarded for thepurpose of generating a candidate intent. In some embodiments, thedetermination of whether a candidate text representation is to bedisregarded can also be based on context information such as usagepattern and/or sensory data, as described in more detail below.

With reference back to FIG. 8, in some embodiments, FTM 840 candetermine, for each of candidate text representations 822, whether aparticular candidate text representation is to be disregarded. If atleast one of the candidate text representations 822 is to bedisregarded, FTM 840 can generate one or more candidate intents 842based on the candidate text representations that are not to bedisregarded. For example, based on the pre-mitigation intents of thecandidate text representations that are not to be disregarded, FTM 840can derive candidate intents 842. Continuing the above example shown inFIG. 9, an FTM of the virtual assistant operating on device 900determines that the first candidate text representation (e.g., “It'sdark outside.”) is to be disregarded but the second candidate textrepresentation (e.g., “Turn on the light, Siri.”) is not to bedisregarded. Accordingly, in some embodiments, the FTM of the virtualassistant can attempt to associate the pre-mitigation intent of thesecond candidate text representation with one of recognized domain in anontology (e.g., ontology 760) to interpret it to derive a candidateintent. For example, the FTM can select the pre-mitigation intent (e.g.,to turn on the light in the user's living room) corresponding to thesecond candidate text representation as a candidate intent, whilefiltering out the pre-mitigation intent corresponding to the firstcandidate text representation.

With reference to FIG. 8, in some embodiments, FTM 840 can obtainconfidence levels corresponding to each of the one or more candidateintents 842. As described above, for each of the pre-mitigation intents,NLE 820 may generate a confidence level. For example, a particularcandidate text representation may be associated with multiple recognizeddomains and thus multiple pre-mitigation intents may be derived from thesame candidate text representation. NLE 820 and/or FTM 840 can determinea confidence score (e.g., based on the relative importance of itsvarious triggered nodes in the ontology) to select a domain that has thehighest confidence value, and in turn, determine a correspondingcandidate intent.

With reference to FIG. 8, in some embodiments, virtual assistant 800includes a candidate intent evaluator (CIE) 860. CIE 860 can determinewhether one or more candidate intents 842 include at least oneactionable intent. For example, CIE 860 can determine, for each of thecandidate intents 842, whether a task can be performed. In someembodiments, CIE 860 can make such determination without actuallyperforming the task (e.g., to make a dry run). As one example, CIE 860can include one or more submodules, or variations thereof, of digitalassistant module 726, such as task flow processing module 736 and dialogprocessing module 734 as shown in FIG. 7B. CIE 860 can perform thedetermination of whether a candidate intent 842 is an actionable intentusing these submodules, or variations thereof, without providing theresults to the speech synthesis module 740 and without outputting theresults.

As another example shown in FIG. 8, CIE 860 can include task flowprocessing module 862 and dialog processing module 864 for determiningwhether a candidate intent 842 is an actionable intent. Task flowprocessing module 862 and dialog processing module 864 shown in FIG. 8can be duplicates of task flow processing module 736 and dialogprocessing module 734 as shown in FIG. 7B, except task flow processingmodule 862 and dialog processing module 864 may not provide theirprocessing results to a speech synthesis module or otherwise output theresults to the user. As a result, CIE 860 can determine whether acandidate intent 842 is an actionable intent and such determination canbe made without virtual assistant 8000 actually performing the task.Thus, if a task cannot be performed or can only be partially performed,CIE 860 can make such determination without causing virtual assistant800 to actually perform the task, but rather would attempt to obtainmore information (e.g., obtaining context information or initiating adialog with the user to obtain more information) for actually performingthe task. Determining whether a candidate intent is an action intentwithout actually performing a task improves the operational efficiencyand enhances the human-machine interface. For example, suchdetermination saves power and avoids outputting a partially performedtask or causing confusion to the user.

Continuing the example shown in FIG. 9, as described above, based on acandidate text representation (e.g., “Turn on the light, Siri.”), an FTMof the virtual assistant operating on device 900 generates a candidateintent of turning on the light in the user's living room. A CIE (e.g.,CIE 860) of the virtual assistant determines whether a task can beperformed with respect to this candidate intent. In some embodiments,the CIE can perform such determination using a task flow processingmodule (e.g., task flow processing module 862) and/or a dialogprocessing module (e.g., dialog processing module 864). For instance,the task flow processing module may attempt to perform the task ofturning on lights in the user's living room based on a structured querygenerated using the corresponding candidate intent. In some examples,the task flow processing module may determine that such task cannot beperformed because it cannot find a home automation device that controlsthe lights in the living room. In some examples, the task flowprocessing module may determine that a home automation device thatcontrols the lights is available and therefore a corresponding task canbe performed.

With reference to FIG. 8, in some embodiments, if CIE 860 determinesthat a task corresponding to a candidate intent 842 can be performed, itdetermines that the particular candidate intent 842 is an actionableintent. CIE 860 can repeat the determination for each of candidateintents 842 and generate actionable intents 868. As illustrated in FIG.8, CIE 860 provides each of actionable intents 868 to task executionmodule 880, which performs one or more tasks according to actionableintents 868. Task execution module 880 can include one or moresubmodules, or variations thereof, of digital assistant module 726, suchas task flow processing module 736 and dialog processing module 734 asshown in FIG. 7B. Unlike CIE 860, which does not cause a task to beactually performed, task execution module 880 receives a structuredquery (or queries) according to actionable intents 868, completes thestructured query, if necessary, and performs the tasks required to“complete” the user's ultimate request. Continuing the example shown inFIG. 9, upon receiving an actionable intent of turning on the light inthe user's living room, a task execution module of the virtual assistantoperating on device 900 can cause the light to turn on and optionallyoutput a result of execution of the actionable intent (e.g., provide anaudio and/or visual output 942 such as “Your light is on.”).

With reference back to FIG. 8, in some embodiments, CIE 860 candetermine whether a task can be performed based on an estimation of aconfidence level associated with performing the task. For example, CIE860 can estimate a confidence level associated with performing a taskand determine whether the confidence level associated with performingthe task satisfies a threshold confidence level. If CIE 860 determinesthat the confidence level associated with performing the task satisfiesthe threshold confidence level, CIE 860 determines that the task can beperformed.

FIG. 10 illustrates a block diagram of an exemplary virtual assistant1000 for providing natural language interaction using execution resultsand context information. In some examples, virtual assistant 1000 (e.g.,digital assistant system 700) can be implemented by a user deviceaccording to various embodiments. In some embodiments, the user device,a server (e.g., server 108), or a combination thereof, can implementvirtual assistant 1000. The user device can be implemented using, forexample, device 104, 200, 400, 900, 1120, 1220, 1320, or 1420 asillustrated in FIGS. 1, 2A-2B, 4, 9, 11A-11B, 12A-12D, 13A-13B, and14A-14D. Similar to virtual assistant 800, virtual assistant 1000includes input module 810, natural language engine 816, FTM 840, CIE860, and task execution module 880. These modules or components ofvirtual assistant 1000 are similar to those described above with respectto virtual assistant 800, and are therefore not repeatedly described. Insome embodiments, one or more of the various modules, models,applications, vocabularies, and user data of virtual assistant 1000 canreceive additional information, such as previous execution resultsand/or context information, to assist in voice activity detection,candidate text representations determination, candidate intentdetermination, actionable intent determination, and/or execution of theactionable intents.

With reference to FIGS. 10 and 11A, for example, a virtual assistant(e.g., virtual assistant 1000) operating on device 1120 receives, via aninput module (e.g., input module 810), an audio stream 1112 from user1110. Audio stream 1112 includes one or more user utterances such as“How is the weather today, Siri? And do you know what the stock priceis?” The virtual assistant generates speech results using the utterancesincluded in audio stream 1112. Based on the speech results, the virtualassistant generates, via a natural language engine (e.g., naturallanguage engine 820), one or more candidate text representations (e.g.,candidate text representations 1022).

Next, for each candidate text representation of the candidate textrepresentations, the virtual assistant determines, via an FTM (e.g., FTM840), whether the particular candidate text representation is to bedisregarded by the virtual assistant. As described above, in someembodiments, the virtual assistant can determine, for example, whether aparticular candidate text representation includes a lexical trigger. Asan example shown in FIG. 11A, the candidate text representationsrepresenting audio stream 1112 include a first candidate textrepresentation (e.g., “How is the weather today, Siri?”) and a secondcandidate text representation (e.g., “And do you know what is the stockprice?”). The virtual assistant determines that the first candidate textrepresentation includes a lexical trigger (e.g., “Siri”) and thereforeis not to be disregarded because the corresponding utterance is directedto the virtual assistant operating on device 1120. With respect to thesecond candidate text representation, the virtual assistant determinesthat it does not include a lexical trigger. In accordance with such adetermination, the virtual assistant estimates a likelihood that theutterance corresponding to the second candidate text representation isnot directed to virtual assistant 1000.

With reference to FIGS. 10 and 11A, in some embodiments, to estimate thelikelihood that the utterance corresponding to the second candidate textrepresentation is not directed to the virtual assistant, the virtualassistant obtains context information (e.g., execution results andcontext information 1044). The context information can be associatedwith a usage pattern of the virtual assistant operating on device 1120.A usage pattern of the virtual assistant can indicate a pattern of aparticular activity that user 1110 performs using the virtual assistantand/or using device 1120 as shown in FIG. 11A. For example, user 1110may frequently ask the same question about weather and stock pricearound 6 AM in the morning. Therefore, a usage pattern may be generatedwith respect to the user's activity (e.g., asking virtual assistant ofthe same question) at or around 6 AM in the morning. The contextinformation can include such a usage pattern and a time 1122 (e.g.,indicating it is about 6 AM in the morning) obtained from device 1120.Based on the context information associated with the usage pattern, thevirtual assistant, via an FTM (e.g., FTM 840) can estimate thelikelihood that the utterance corresponding to the second candidate textrepresentation is not directed to the virtual assistant. For example,based on the context information indicating that the user frequentlyasks the virtual assistant about the stock price around 6 AM in themorning and that the current time is about 6 AM, the virtual assistantcan estimate that the likelihood the utterance “And do you know what isthe stock price?” is not directed to the virtual assistant 1000 is low(e.g., does not satisfy a threshold). As a result, the virtual assistantdetermines, based on the estimated likelihood, that the second candidatetext representation is not to be disregarded.

In some embodiments, context information can also be used fordetermining whether a candidate intent is an actionable intent.Continuing the above example shown in FIG. 11A, based on thedetermination that both the first candidate text representation (e.g.,“How is the weather today, Siri?”) and the second candidate textrepresentation (e.g., “And do you know what is the stock price?”) arenot to be disregarded, the virtual assistant operating on device 1120generates one or more candidate intents. For example, the virtualassistant generates a first candidate intent of obtaining weatherinformation for today and a second candidate intent of obtaining stockprice information. For each of the candidate intents, the virtualassistant, via a CIE (e.g., CIE 860), determines whether a task can beperformed. As described above, a CIE can make such determination byusing, for example, a task flow processing module and/or a dialogprocessing module without actually performing the task.

In some embodiments, with respect to a first candidate intent ofobtaining weather information, the virtual assistant determines thatadditional information of location may be necessary to perform a task ofobtaining weather information. This location information, however, isnot presented in or provided by the first candidate text representation.Accordingly, in some embodiments, the virtual assistant can obtaincontext information associated with sensory data from one or moresensors communicatively coupled to device 1120 and determine whether thetask can be performed based on the context information. For example,location data may be obtained as the context information indicating thecurrent location of device 1120. With this context information, thevirtual assistant determines that the first candidate intent ofobtaining weather information is an actionable intent, because weatherinformation can be obtained from an internal or external data source(e.g., from a weather information website) for the current location ofdevice 1120.

With reference to FIG. 11A, with respect to a second candidate intent ofobtaining stock price information, the virtual assistant determines thatadditional information of the name of the stock may be necessary toperform a task of obtaining stock price. This stock name information,however, is not presented in or provided by the second candidate textrepresentation. Accordingly, in some embodiments, the virtual assistantcan obtain context information associated with a usage pattern of thevirtual assistant and/or device 1120. A usage pattern of the virtualassistant and/or device 1120 can indicate a pattern of a particularactivity that user 1110 performs using virtual assistant 1000 and/orusing the user's device 1120 as shown in FIG. 11A. For example, user1110 may frequently ask the question about the S&P index around 6 AM inthe morning. Therefore, a usage pattern may be generated with respect tothe user's activity (e.g., asking the virtual assistant of the samequestion of S&P 500 index) at or around 6 AM in the morning. The contextinformation can include such a usage pattern and a time 1122 (e.g.,indicating it is about 6 AM in the morning) provided by device 1120.With this context information, the virtual assistant determines that thesecond candidate intent of obtaining stock price is an actionableintent, because the S&P 500 index information can be obtained.

FIG. 11B illustrates an exemplary user interface for providing naturallanguage interaction by a virtual assistant (e.g., virtual assistant1000 shown in FIG. 10) using context information associated with sensorydata. As illustrated in FIGS. 10 and 11B, for example, the virtualassistant operating on device 1120 receives one or more audio streamsfrom user 1110 and user 1130. An audio stream may include, for example,an utterance 1132 from user 1130, an utterance 1134 from user 1130, andan utterance 1136 from user 1110. One or more utterances from user 1110may be directed to the virtual assistant or user 1130. For example,utterance 1132 from user 1110 may include “Siri, what is the S&P indextoday?” Similar to those described above, the virtual assistantdetermines that a first candidate text representation of utterance 1132includes a lexical trigger (e.g., “Siri”) and therefore is not to bedisregarded. Based on such a determination, the virtual assistantgenerates a first candidate intent and determines that the firstcandidate intent of obtaining S&P 500 index is actionable. Accordingly,the virtual assistant performs the task of obtaining the S&P 500 indexand outputs a result 1138 (e.g., an audio and/or visual outputindicating “The S&P 500 Index is at 12000 today.”).

As shown in FIG. 11B, upon hearing the S&P 500 index outputted byvirtual assistant 1000, user 1130 may provide an utterance 1134 askinguser 1130 “Did you hear the AAPL went up a lot today?” The virtualassistant operating on device 1120 can generate a second candidate textrepresentation corresponding to utterance 1134 and determine whether thesecond candidate text representation is to be disregarded. For example,the virtual assistant may determine that the second candidate textrepresentation does not include a lexical trigger, and thereforeestimate the likelihood utterance 1134 is not directed to virtualassistant. In some embodiments, to estimate the likelihood, the virtualassistant can obtain sensory data from one or more sensorscommunicatively coupled to device 1120 and estimate, based on theobtained sensory data, the likelihood that utterance 1134 is notdirected to the virtual assistant. For example, device 1120 can includean optical sensor 264 (e.g., a camera) that detects user 1110's eye gazewith respect to device 1120 at any given time. Optical sensor 264 maydetect, for example, that user 1130 is not looking at device 1120 whileutterance 1134 is received by the virtual assistant operating on device1120. As a result, the virtual assistant estimates, using the sensorydata provided by optical sensor 264, that the likelihood utterance 1134is not directed to the virtual assistant is high (e.g., comparing to athreshold). Accordingly, FTM 840 determines the second candidate textrepresentation is to be disregarded.

Continuing with the example shown in FIG. 11B, upon hearing utterance1134, user 1130 may provide utterance 1136 “Really, what is the pricefor AAPL today?” The virtual assistant can generate a third candidatetext representation corresponding to utterance 1136 and determinewhether the third candidate text representation is to be disregarded.Similarly to those described above, the virtual assistant can determinethat the third candidate text representation does not include a lexicaltrigger. Based on such a determination, the virtual assistant canestimate the likelihood utterance 1136 is not directed to the virtualassistant operating on device 1120. In some embodiments, to estimate thelikelihood, the virtual assistant obtains sensory data and estimate,based on the obtained sensory data, the likelihood that utterance 1136is not directed to the virtual assistant 1000. For example, opticalsensor 264 may detect, for example, that user 1110 is looking at device1120 while utterance 1136 is received by the virtual assistant. As aresult, the virtual assistant estimates, using the sensory data providedby optical sensor 264, that the likelihood utterance 1136 is notdirected to virtual assistant 1000 is low (e.g., comparing to alikelihood threshold). Accordingly, the virtual assistant determines thethird candidate text representation is not to be disregarded. Thevirtual assistant can therefore generate a candidate intent based on thethird candidate text representation and determine that candidate intentis an actionable intent. Accordingly, the virtual assistant performs thetask to obtain the stock price of AAPL and outputs a result 1140 (e.g.,an audio and/or visual output indicating “AAPL closed at $200 today.”).

FIGS. 12A-12D illustrates exemplary user interfaces for providingnatural language interaction by a virtual assistant operating on device1220 using context information associated with executing a previouslydetermined actionable intent. As shown in FIGS. 10 and 12A, user 1210provides a first audio stream 1212 (e.g., “Siri, play some music”),which is received by the virtual assistant operating on device 1220.Similar to those described above, the virtual assistant determines thatfirst audio stream 1212 includes a lexical trigger (e.g., “Siri”),generates a candidate text representation of first audio stream 1212(e.g., “Siri, play some music.”), determines that the candidate textrepresentation of first audio stream 1212 is not to be disregardedbecause it includes the lexical trigger, generates a candidate intent,determines that the candidate intent is an actionable intent (e.g., atask of playing music can be performed), executes the actionable intent,and outputting a result of the execution (e.g., outputting an audioand/or visual message 1224 such as “Here is some music you mightlike.”). As illustrated in FIG. 12A, virtual assistant 1000 may receiveand process first audio stream 1212 at a time 1222A (e.g., 9 AM).

As illustrated in FIG. 12B, while the virtual assistant operating ondevice 1220 is executing the task of playing music, it may receive asecond audio stream 1214 from user 1210 at a time 1222B (e.g., 9:05 AM).In some embodiments, the virtual assistant generates a candidate textrepresentation of the second audio stream 1214. Based on the candidatetext representation of second audio stream 1214, the virtual assistantcan determine whether second audio stream 1214 (e.g., “Stop”) is a partof a same audio session as first audio stream 1212 (e.g., “Play somemusic”).

In some embodiments, to determining whether second audio stream 1214 isa part of the same audio session that includes first audio stream 1212,the virtual assistant operating on device 1220 can obtain contextinformation associated with executing a previously determined actionableintent. For example, the virtual assistant detects that when secondaudio stream 1214 is received, music is playing as a result of executingthe previous determined actionable intent. As a result, the virtualassistant determines that second audio stream 1214 is or is likely apart of the same audio session that includes first audio stream 1212. Insome embodiments, such a determination may also taking into account of arelation (e.g., semantic relation, topical relation) between first audiostream 1212 and second audio stream 1214. Embodiments that taking intoaccount of a relation is further described in detail below.

With reference to FIG. 12B, in accordance with a determination that thesecond audio stream 1214 is a part of the same audio session thatincludes first audio stream 1212, the virtual assistant generates, basedon the candidate text representation of second audio stream 1214, asecond candidate intent. The virtual assistant further determineswhether the second candidate intent includes an actionable intent. Insome embodiments, this determination can be based on context informationassociated with a previous task performed or is being performed by thevirtual assistant. For example, the context information can beassociated with performing the previous task of playing music. As shownin FIG. 12B, the second candidate intent may thus be determined to bestopping the music that is currently playing. The virtual assistant canthus determine that a task can be performed (e.g., stop playing themusic) according to this second candidate intent, and therefore thesecond candidate intent is actionable. As a result, the virtualassistant executes the actionable intent to stop playing the music. Insome embodiments, the virtual assistant optionally provides an audioand/or visual output 1226 (e.g., “OK”) indicating the results ofexecution.

FIGS. 12C and 12D illustrate a similar scenario where the virtualassistant operating on device 1220 receives a first audio stream 1242including an utterance such as “Siri, play some music.” Similar to thosedescribed above, the virtual assistant determines an actionable intentof playing music, executes the actionable intent, and optionally outputan audio/visual message 1222 (e.g., “Here is some music you mightlike.”). While the music is still playing, the virtual assistantreceives a second audio stream 1244 including an utterance such as “Skipthis song.” The virtual assistant detects that when second audio stream1244 is received, music is playing as a result of executing the previousdetermined actionable intent. As a result, the virtual assistant candetermine that second audio stream 1244 is or is likely a part of thesame audio session that includes first audio stream 1242. As a result,the virtual assistant generates, based on the candidate textrepresentation of second audio stream 1244, a second candidate intent.The virtual assistant further determines whether the second candidateintent includes an actionable intent. For example, as shown in FIG. 12D,the second candidate intent may be determined to be skipping sound track1 that is currently playing and starting to play the next sound track.The virtual assistant can thus determine that a task can be performedaccording to this second candidate intent, and therefore the secondcandidate intent is actionable. As a result, the virtual assistantexecutes the actionable intent to stop playing sound track 1 and startplaying sound track 2.

FIGS. 13A-13B illustrates exemplary user interfaces for providingnatural language interaction by a virtual assistant using contextinformation associated with a relation of user utterances or audiostreams. As described above, context information can be provided to oneor more modules or components of a virtual assistant (e.g., FTM 840 andClE 860 of virtual assistant 1000 shown in FIG. 10). In someembodiments, context information can be provided to a CIE (e.g., CIE860) for determining whether a candidate intent is an actionable intent.Such context information can include, for example, one or more relationsamong candidate text representations representing the user utterances inan audio stream.

As an example shown in FIG. 13A, a virtual assistant (e.g., virtualassistant 1000) operating on device 1320 receives an audio stream 1312.Audio stream 1312 may include a first utterance (e.g., “You knowwarriors did great in their last game.”) and a second utterance (e.g.,“When is the next game, Siri?”). Based on audio stream 1312, candidatetext representations can be generated to represent the utterancesincluded in audio stream 1312. The virtual assistant can determine(e.g., using FTM 840) that the candidate text representation of thefirst utterance (e.g., “You know warriors did great in their lastgame.”) does not include a lexical trigger and that the first utteranceis likely not directed to the virtual assistant. As a result, thevirtual assistant can determine that the candidate text representationof the first utterance is to be disregarded for the purpose ofgenerating candidate intents. In some embodiments, while the candidatetext representation of the first utterance is disregarded for thepurpose of generating candidate intents, it can be retained fordetermining relations among multiple candidate text representations.

Continuing with the example shown in FIG. 13A, the virtual assistant candetermine (e.g., using FTM 840) that the candidate text representationof the second utterance (e.g., “When is the next game, Siri?”) includesa lexical trigger and thus the second utterance is likely directed tothe virtual assistant. As a result, the candidate text representation ofthe second utterance is not to be disregarded. Accordingly, the virtualassistant generates a candidate intent based on the candidate textrepresentation of the second utterance, and determines whether thecandidate intent is an actionable intent. As described above, todetermine whether a candidate intent is an actionable intent, in someembodiments, the virtual assistant determines whether a task can beperformed according to the actionable intent. The determination ofwhether a task can be performed can be based on the candidate textrepresentation of the second utterance and based on context informationindicating relations among candidate text representations.

As described above, the candidate text representation of the secondutterance in audio stream 1312 may include “When is the next game,Siri?” Based solely on this candidate text representation, the virtualassistant may not be able to determine whether a task can be performedbecause the candidate text representation does not indicate which gamethe user refers to. In some examples, the virtual assistant candetermine relations among the adjacent candidate text representations.For example, the virtual assistant determines that the candidate textrepresentation of the first utterance (e.g., “You know warriors didgreat in their last game.”) is semantically, topically, and/ortemporally related to the candidate text representation of the secondutterance (e.g., “When is the next game, Siri?”). As a result, thevirtual assistant can use context information associated with therelations among the candidate text representations to determine whethera task can be performed. In this example, the context informationindicates that user 1310 is likely referring to the Golden StateWarriors game when user 1310 asks “When is the next game, Siri?” As aresult, the virtual assistant determines that a task can be performedbased on the context information (e.g., a task to obtain the schedule ofthe Warriors game can be performed by searching the Internet) and thusdetermines the candidate intent is an actionable intent. Accordingly,the virtual assistant can execute the actionable intent and provides,for example, an audio and/or visual output 1326 such as “Golden StateWarriors next game is schedule tomorrow at 5:30 pm.”

Turning now to FIG. 13B, context information associated with relations(e.g., semantic, topical, temporal) among multiple audio streams canalso be used to determine whether two or more audio streams are in thesame audio session. With reference to FIG. 13B, the virtual assistant(e.g., virtual assistant 1000 shown in FIG. 10) operating on device 1320receives a first audio stream 1342 including an utterance such as “Whatwas the score of the Warriors' game?” Similar to those described above,the virtual assistant generates a candidate text representation of firstaudio stream 1342, determines that it is not to be disregarded,generates a candidate intent, determines that the candidate intent is anactionable intent, executes the actionable intent, and provides an audioand/or visual output 1346 such as “Warriors defeated Knicks by a scoreof 123 to 112.”

In some embodiments, while or after the virtual assistant outputs aresult of execution of the actionable intent corresponding to firstaudio stream 1342, the virtual assistant operating on device 1320receives a second audio stream 1344 including an utterance such as “Whenis the next game?” In some embodiments, the virtual assistant generatesa candidate text representation to represent second audio stream 1344.The generation of the candidate text representation of second audiostream 1344 can be performed irrespective of whether second audio stream1344 includes a lexical trigger. For example, the virtual assistant candetermine a temporal relation between receiving second audio stream 1344and the execution of the actionable intent corresponding to first audiostream 1342. The virtual assistant further determines that the temporalrelation indicates that second audio stream 1344 is received within athreshold time period from the execution of the actionable intent, andtherefore a lexical trigger in second audio stream 1344 is not requiredfor generating a candidate text representation of second audio stream1344.

After generation of the candidate text representation of second audiostream 1344, the virtual assistant operating on device 1320 candetermine whether second audio stream 1344 is a part of a same audiosession that includes first audio stream 1342. The virtual assistant candetermine relations among the respective candidate text representationsof first audio stream 1322 and second audio stream 1344. For example,the virtual assistant determines that the candidate text representationof the first audio stream 1322 (e.g., “What was the score of theWarriors game?”) is semantically, topically, and/or temporally relatedto the candidate text representation of the second audio stream (e.g.,“When is the next game?”). As a result, the virtual assistant can usecontext information associated with the relations among the respectivecandidate text representations of the first and second audio streams todetermine whether the audio streams are in the same audio session. Inthe example shown in FIG. 13B, the virtual assistant determines thatsecond audio stream 1344 is a part of the same audio session thatinclude first audio stream 1322, because the respective candidate textrepresentations of the audio streams are semantically and/or topicallyrelated. In accordance with such a determination, the virtual assistantgenerates a candidate intent based on the candidate text representationsof second audio stream 1322, determines whether the candidate intent isan actionable intent, and if so, executes the actionable intent. As aresult, the virtual assistant provides an audio and/or visual output1348 such as “Golden State Warriors next game is scheduled tomorrow, at5:30 pm.”

FIGS. 14A-14D illustrate exemplary user interfaces for selecting a taskfrom a plurality of tasks using context information. As described above,previous execution results and/or context information can be provided toone or more modules or components of a virtual assistant (e.g., FTM 840and CIE 860 of virtual assistant 1000 shown in FIG. 10). In someembodiments, context information can be provided to task executionmodule 880 for the purpose of selecting one or more tasks to perform.With reference to FIG. 14A, a virtual assistant operating on device 1420receives a first audio stream 1412 including an utterance such as “Siri,play some music.” Similar to those described above with respect to FIG.12A, the virtual assistant determines that audio stream 1412 includes alexical trigger, generates a candidate text representation, determinesthat the candidate text representation is not to be disregarded,generates a candidate intent, determines that the candidate intent is anactionable intent, and executes the actionable intent. As a result, thevirtual assistant starts to play music and optionally provides an audioand/or visual output 1424 such as “Here is some music you might like.”

With reference to FIG. 14B, in some embodiments, while the virtualassistant is executing the previously determined actionable intent(e.g., playing music), it receives a second audio stream 1414. Secondaudio stream 1414 may include another user request, which may notsemantically or topically relate to first audio stream 1412. Forexample, second audio stream 1414 includes an utterance such as “I wouldlike to send a message to my wife, Siri.” The virtual assistantoperating on device 1420 can repeat a process similar to those describedabove and execute a second actionable intent derived based on secondaudio stream 1414. As a result, the virtual assistant causes device 1420to display, for example, a text message user interface 1426 forcomposing a text message.

Turning to FIG. 14C, in some embodiments, while executing both of thepreviously determined actionable intents (e.g., playing music anddisplaying a text message user interface for composing a text message),the virtual assistant operating on device 1420 receives a third audiostream 1416. Third audio stream 1416 may be related to the execution ofone or both of the previously determined actionable intents. Forexample, third audio stream 1416 may include an utterance such as“stop.” Based on third audio stream 1416, the virtual assistant candetermine, for example, a first candidate intent of stopping thecurrently-playing music and a second candidate intent of stoppingcomposing of the text message. The virtual assistant can furtherdetermine that both the first and second candidate intents areactionable intents because a respective task can be performed.

In some embodiments, if there is a plurality of actionable intents, thevirtual assistant selects, from a plurality of tasks associated with theplurality of actionable intents, a single task to perform. In someembodiments, the selection of a single task for execution can be basedon context information. For example, the virtual assistant obtainscontext information associated with a most-recent task initiated by thevirtual assistant and selects a single task for execution based on suchcontext information. In this example as described above, the most-recenttask initiated by the virtual assistant is to display a user interfacefor composing a text message. Accordingly, based on the contextinformation associated with this most-recent task of displaying userinterface for composing a text message, the virtual assistantdisambiguates the user request included in third audio stream 1416 andselects the task of stopping or cancelling the composing of the textmessage. Optionally, as shown in FIG. 14C, the virtual assistant canprovide an audio and/or visual output 1432 such as “OK, message to yourwife cancelled.”

In some embodiments, if there is a plurality of actionable intents, thevirtual assistant selects, from a plurality of tasks associated with theplurality of actionable intents, a single task to perform based on auser selection. This is illustrated in FIG. 14D, where the virtualassistant outputs a plurality of task options. For example, the virtualassistant provides an audio and/or visual output 1434 (e.g., “Would youlike to stop the music or cancel the message to your wife?”) promptinguser 1410 to select a task. The virtual assistant receives a userselection in an audio stream 1418 (e.g., “stop the music”) and performsthe task based on the user selection. Optionally, as shown in FIG. 14D,the virtual assistant can provide an audio and/or visual output 1436such as “Music stopped.”

In some embodiments, if there is a plurality of actionable intents, thevirtual assistant selects, from a plurality of tasks associated with theplurality of actionable intents, a single task to perform based on apriority associated with each of the plurality of tasks. For example, avirtual assistant (e.g., virtual assistant 1000) may receive a firstaudio stream including an utterance such as “How is the weather today,Siri?” Before the virtual assistant responds, it receives a second audiostream including utterances such as “There is a car accident! Siri, call911!” Based on the audio streams, the virtual assistant determines afirst actionable intent of obtaining weather information and a secondactionable intent of making an emergency call. In some embodiments, thevirtual assistant can determine a priority associated with each of theplurality of tasks to be performed and select the task based on thedetermined priorities. In the above example, while the second audiostream is received after receiving the first audio stream, the virtualassistant determines that the task associated with the second actionableintent of making an emergency 911 call has a higher priority than thetask associated with the first actionable intent of obtaining weatherinformation. Accordingly, the virtual assistant selects the taskassociated with the second actionable intent for execution (e.g., makingan emergency 911 call).

While the various embodiments described above relate to specific type ofcontext information, it is appreciated that the techniques described canalso use any types of context information, as described in U.S. patentapplication Ser. No. 15/694,267, “Methods and Systems for CustomizingSuggestions Using User-specific Information,” filed Sep. 1, 2017, whichis hereby incorporated by reference in its entirety.

5. Process for Providing Natural Language Interaction

FIG. 15A-15G illustrates process 1500 for operating a virtual assistantfor providing natural language interaction, according to variousembodiments. Process 1500 is performed, for example, using one or moreelectronic devices implementing a virtual assistant. In some examples,process 1500 is performed using a client-server system (e.g., system100), and the blocks of process 1500 are divided up in any mannerbetween the server (e.g., DA server 106) and a client device. In otherexamples, the blocks of process 1500 are divided up between the serverand multiple client devices (e.g., a mobile phone and a smart watch).Thus, while portions of process 1500 are described herein as beingperformed by particular devices of a client-server system, it will beappreciated that process 1500 is not so limited. In other examples,process 1500 is performed using only a client device (e.g., user device104, 200, 400, 600, 900, 1120, 1220, 1320, or 1420) or only multipleclient devices. In process 1500, some blocks are, optionally, combined,the order of some blocks is, optionally, changed, and some blocks are,optionally, omitted. In some examples, additional steps may be performedin combination with the process 1500.

As described above, always requiring a trigger phrase at the beginningportion of an utterance from the user can cause the human-machineinteraction to become cumbersome and make the human-machine userinterface less natural and efficient. The techniques described in thisapplication, including those represented by process 1500, eliminate orreduce the need of this requirement to lead every user utterance with atrigger phrase. Instead, a trigger word or phrase can be placed in anyportion of an audio stream that may include one or more user utterances.Moreover, the techniques described in this application do not requireusing a trigger phrase that include a plurality of words (e.g., “HeySiri”). A single word (e.g., “Siri”) can be used to indicate that theaudio stream including the user utterances is directed to the virtualassistant. This enables a more natural way of communication. As aresult, the techniques enhance the operability of the device and makethe user-device interface more efficient which, additionally, reducespower usage and improves battery life of the device by enabling the userto use the device more quickly and efficiently.

With reference to FIG. 15A, at block 1502, first audio stream (e.g.,audio stream 912 as illustrated in FIG. 9) including one or moreutterances is received via a microphone. At block 1504, whether thefirst audio stream includes a lexical trigger is determined. In someembodiments, the lexical trigger is a single-word lexical trigger. Insome embodiments, the first audio stream includes a first utterance andthe single-word lexical trigger is positioned in a portion of the firstutterance other than the beginning portion of the first utterance. As anexample described in FIG. 9, the lexical trigger (e.g., “Siri”) ispositioned at the end of an utterance (e.g., “Turn on the light,Siri.”).

At block 1506, to determine whether the first audio stream includes alexical trigger, a beginning point of the first audio stream isdetected. As one example of detecting the beginning point of the firstaudio stream, at block 1508, an absence of voice activity beforereceiving the first audio stream is detected. At block 1510, whether theabsence of voice activity before receiving the first audio streamexceeds a first threshold period of time is determined. At block 1512,in accordance with a determination that the absence of voice activityexceeds the first threshold period of time, the beginning point of thefirst audio stream is determined based on the absence of voice activitybefore receiving the first audio stream.

At block 1514, to determine whether the first audio stream includes alexical trigger, an end point of the first audio stream is detected. Asone example of detecting the end point of the first audio stream, atblock 1516, an absence of voice activity after receiving the one or moreutterances of the first audio stream is detected via the microphone. Atblock 1518, whether the absence of voice activity after receiving theone or more utterances of the first audio stream exceeds a secondthreshold period of time is determined. At block 1520, in accordancewith a determination that the absence of voice activity after receivingthe one or more utterances of the first audio stream exceeds the secondthreshold period of time, the end point of the first audio stream isdetermined based on the absence of voice activity after receiving theone or more utterances of the first audio stream.

As another example of detecting the end point of the first audio stream,at block 1522, a pre-configured duration that the electronic device isconfigured to receive the first audio stream is obtained. With referenceto FIG. 15B. at block 1524, the end point of the first audio stream isdetermined based on the detected beginning point of the first audiostream and the pre-configured duration.

As another example of detecting the end point of the first audio stream,at block 1526, a size of an audio file representing the received one ormore utterances of the first audio stream is determined. At block 1528,the size of the audio file is compared with a capacity of a bufferstoring the audio file. At block 1530, the end point of the first audiostream is determined based on a result of comparing the size of theaudio file with the capacity of the buffer storing the audio file.Various embodiments of detecting of the beginning point and the endpoint of an audio stream are described in detail above with respect to,for example, FIGS. 8 and 9.

At block 1532, to determine whether the first audio stream includes alexical trigger (e.g., “Siri”), whether a lexical trigger is includedbetween the beginning point and the end point of the first audio streamis determined.

At block 1534, in accordance with a determination that the first audiostream includes the lexical trigger, one or more candidate textrepresentations of the one or more utterances are generated. Asdescribed above with respect to FIG. 8, candidate text representationscan be generated by a natural language engine (e.g., NLE 820).

At block 1536, to generate the one or more candidate textrepresentations, speech-to-text conversion of each of the one or moreutterances of the first audio stream to generate the one or morecandidate text representations is performed. For example, speech-to-textconversion of each of the utterances of audio stream 912 shown in FIG. 9can be performed to generate candidate text representations including afirst candidate text representation (e.g., “It's dark outside.”) and asecond candidate text representation (e.g., “Turn on the light, Siri.”).At block 1538, confidence levels corresponding to the one or morecandidate text representations are determined.

With reference to FIG. 15C, at block 1540, whether at least onecandidate text representation of the one or more candidate textrepresentations is to be disregarded by the virtual assistant isdetermined. At block 1542, to determine whether at least one candidatetext representation of the one or more candidate text representations isto be disregarded by the virtual assistant, whether the at least onecandidate text representation includes the lexical trigger isdetermined. At block 1544, in accordance with a determination that theat least one candidate text representation does not include the lexicaltrigger, a likelihood that the utterance corresponding to the at leastone candidate text representation is not directed to the virtualassistant is estimated. As described above, in the example shown in FIG.9, an FTM of the virtual assistant operating on device 900 determinesthat a candidate text representation (e.g., “It's dark outside.”) doesnot include a lexical trigger. Further, using a decision tree, the FTMof the virtual assistant can estimate a likelihood that the utterancecorresponding to the particular first candidate text representation isnot directed to the virtual assistant.

As one example of estimating the likelihood that the utterancecorresponding to the at least one candidate text representation is notdirected to the virtual assistant, at block 1546, context informationassociated with a usage pattern (e.g., frequency of a particularquestion is asked at a particular time, as illustrated in FIGS. 10 and11A) of the virtual assistant is obtained. At block 1548, based on thecontext information associated with the usage pattern of the virtualassistant, the likelihood that the utterance corresponding to the atleast one candidate text representation is not directed to the virtualassistant is estimated.

As another example of estimating the likelihood that the utterancecorresponding to the at least one candidate text representation is notdirected to the virtual assistant, at block 1550, sensory data (e.g.,location data as described above) from one or more sensorscommunicatively coupled to the electronic device are obtained. At block1552, based on sensory data, the likelihood that the utterancecorresponding to the at least one candidate text representation is notdirected to the virtual assistant is estimated.

As another example of estimating the likelihood that the utterancecorresponding to the at least one candidate text representation is notdirected to the virtual assistant, a degree that a candidate textrepresentation conforms to a language model (LM) and/or a context freegrammar (CFG) corresponding to recognized/valid requests for virtualassistant is determined. For example, a candidate text representationsuch as “Eat your vegetables” may have a low degree of conformation toan LM and/or CFG, and is thus not less likely directed to the virtualassistant. Another candidate text representation such as “Book a tablefor two” may have a high degree of conformation to an LM and/or CFG, andis thus more likely directed to the virtual assistant.

At block 1554, based on the estimated likelihood, whether the at leastone candidate text representation of the one or more candidate textrepresentations is to be disregarded by the virtual assistant isdetermined. As one example described above with respect to FIG. 1A,based on the context information indicating that the user frequentlyasks the virtual assistant about the stock price around 6 AM in themorning and that the current time is about 6 AM, the virtual assistantcan estimate that the likelihood the utterance “And do you know what isthe stock price?” is not directed to the virtual assistant is low (e.g.,does not satisfy a threshold). As a result, the virtual assistantdetermines, based on the estimated likelihood, that the particularcandidate text representation is not to be disregarded.

With reference to FIG. 15D, at block 1556, in accordance with adetermination that at least one candidate text representation is to bedisregarded by the virtual assistant, one or more candidate intents aregenerated based on candidate text representations of the one or morecandidate text representations other than the to be disregarded at leastone candidate text representation. To generate the candidate intents, atblock 1558, one or more pre-mitigation intents corresponding to the oneor more candidate text representations of the one or more utterances areobtained. At block 1560, from the one or more pre-mitigation intents,the one or more candidate intents corresponding to the one or morecandidate text representations other than the to be disregarded at leastone candidate text representation are selected. As described above withrespect to FIG. 10, candidate intents can be generated by an FTM (e.g.,FTM 1044).

At block 1562, whether the one or more candidate intents include atleast one actionable intent is determined. At block 1564, to determinewhether the one or more candidate intents include at least oneactionable intent, for each of the one or more candidate intents,whether a task can be performed is determined. As described above, insome embodiments, such a determination can be performed by a CIE (e.g.,CIE 860 shown in FIG. 10). As one example of determining whether a taskcan be performed, at block 1566, context information associated with ausage pattern of the virtual assistant is obtained. At block 1568, basedon the context information associated with the usage pattern of thevirtual assistant, whether the task can be performed is determined. Oneexample of such a determination based on usage pattern is illustratedabove with respect to FIG. 11B.

As another example of determining whether a task can be performed, atblock 1570, context information associated with a previous taskperformed by the virtual assistant is obtained. At block 1572, based onthe context information associated with the previous task performed bythe virtual assistant, whether the task can be performed is determined.One example of such a determination based on previous task performed bythe virtual assistant is illustrated above with respect to FIGS.12A-12D.

With reference to FIG. 15E, as another example of determining whether atask can be performed, at block 1574, one or more relations among theone or more candidate text representations is determined. At block 1576,whether the task can be performed based on the one or more relationsamong the one or more candidate text representations is determined. Oneexample of such a determination based on one or more relations isillustrated above with respect to FIGS. 13A-13B.

As another example of determining whether a task can be performed, atblock 1578, sensory data (e.g., location data) from one or more sensorscommunicatively coupled to the electronic device are obtained. At block1580, whether the task can be performed based on the sensory data isdetermined.

As another example of determining whether the task can be performed, atblock 1582, a confidence level associated with performing the task isestimated. At block 1584, whether the confidence level associated withperforming the task satisfies a threshold confidence level isdetermined. At block 1586, in accordance with a determination that theconfidence level associated with performing the task satisfies thethreshold confidence level, it is determined that the task can beperformed.

At block 1588, in accordance with a determination that the task can beperformed, it is determined that the one or more candidate intentsinclude at least one actionable intent. For example, as described abovewith respect to FIGS. 11A, based on context information (e.g., a usagepattern and a time), the virtual assistant determines that a particularcandidate intent of obtaining stock price is an actionable intent.

At block 1590, in accordance with a determination that the one or morecandidate intents include at least one actionable intent, the at leastone actionable intent is executed. At block 1592, one or more tasks areperformed according to the at least one actionable intent.

With reference to FIG. 15F, to execute the at least one actionableintent, at block 1594, a first task for execution is selected from aplurality of tasks associated with the plurality of actionable intents.As one example of selecting the first task for execution, at block 1596,context information associated with a most-recent task initiated by thevirtual assistant (e.g., displaying a user interface for composing atext message as illustrated in FIG. 14B) is obtained. At block 1598, thefirst task is selected based on the context information associated witha previous task performed by the virtual assistant.

As one example of selecting the first task for execution, at block 1600,a plurality of task options corresponding to the plurality of tasksassociated with the plurality of actionable intents is outputted. Atblock 1602, a user selection is received from the plurality of taskoptions. At block 1604, the first task for execution is selected basedon the user selection. Selecting a task for execution based on a userselection is illustrated above with respect to FIG. 14D.

As one example of selecting the first task for execution, at block 1606,a priority associated with each of the plurality of tasks is determined.At block 1608, the first task for execution is selected based on thepriority associated with each of the plurality of tasks. At block 1610,the selected first task is performed. As described above, a task havinga higher priority (e.g., making an emergency call) is selected over atask having a lower priority (e.g., reporting weather information).

At block 1612, a result of the execution of the at least one actionableintent is outputted.

With reference to FIG. 15G, at block 1614, upon executing the at leastone actionable intent, a second audio stream is received via themicrophone. At block 1616, one or more second candidate textrepresentations are generated to represent the second audio stream. Atblock 1618, based on the one or more second candidate textrepresentations, whether the second audio stream is a part of an audiosession that includes the first audio stream is determined.

At block 1620, as one example of determining whether the second audiostream is a part of an audio session that includes the first audiostream, context information associated with executing the at least oneactionable intent is obtained. At block 1622, based on the contextinformation associated with executing the at least one actionableintent, whether the second audio stream is a part of the audio sessionthat includes the first audio stream is determined.

At block 1624, as another example of determining whether the secondaudio stream is a part of an audio session that includes the first audiostream, a relation among respective candidate text representations ofthe first audio stream and the second audio stream is determined. Atblock 1626, based on the relation among respective candidate textrepresentations of the first audio stream and the second audio stream,whether the second audio stream is a part of the audio session thatincludes the first audio stream is determined.

At block 1628, in accordance with a determination that the second audiostream is a part of the audio session that includes the first audiostream, one or more second candidate intents are generated based on theone or more second candidate text representations.

At block 1630, whether the one or more second candidate intents includeat least one second actionable intent is determined. At block 1632, inaccordance with a determination that the one or more second candidateintents include at least one second actionable intent, the at least onesecond actionable intent is executed. At block 1634, a result of theexecution of the at least one second actionable intent is outputted.Examples of the process illustrated in FIG. 15G are described above withrespect to FIGS. 12A-12D.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources (e.g., thecontext information associated with a usage pattern of the virtualassistant or device) to improve human-machine interface to provide amore natural language interaction. The present disclosure contemplatesthat in some instances, this gathered data may include personalinformation data that uniquely identifies or can be used to contact orlocate a specific person. Such personal information data can includedemographic data, location-based data, telephone numbers, emailaddresses, twitter IDs, home addresses, data or records relating to auser's health or level of fitness (e.g., vital signs measurements,medication information, exercise information), date of birth, or anyother identifying information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used todeliver targeted content that is of greater interest to the user.Accordingly, use of such personal information data enables calculatedcontrol of the delivered content. Further, other uses for personalinformation data that benefit the user are also contemplated by thepresent disclosure. For instance, health and fitness data may be used toprovide insights into a user's general wellness, or may be used aspositive feedback to individuals using technology to pursue wellnessgoals.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. Such policies should be easilyaccessible by users, and should be updated as the collection and/or useof data changes. Personal information from users should be collected forlegitimate and reasonable uses of the entity and not shared or soldoutside of those legitimate uses. Further, such collection/sharingshould occur only after receiving the informed consent of the users.Additionally, such entities would take any needed steps for safeguardingand securing access to such personal information data and ensuring thatothers with access to the personal information data adhere to theirprivacy policies and procedures. Further, such entities can subjectthemselves to evaluation by third parties to certify their adherence towidely accepted privacy policies and practices. In addition, policiesand practices should be adapted for the particular types of personalinformation data being collected and/or accessed and adapted toapplicable laws and standards, including jurisdiction-specificconsiderations. For instance, in the US, collection of or access tocertain health data may be governed by federal and/or state laws, suchas the Health Insurance Portability and Accountability Act (HIPAA);whereas health data in other countries may be subject to otherregulations and policies and should be handled accordingly. Hencedifferent privacy practices should be maintained for different personaldata types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof collecting usage pattern of a user's activities, the presenttechnology can be configured to allow users to select to “opt in” or“opt out” of participation in the collection of personal informationdata before or during such a collection. In another example, users canselect not to provide or share the users' activities information. In yetanother example, users can select to limit the length of time the users'activities information is maintained or entirely prohibit thedevelopment of the usage pattern based on the activities information. Inaddition to providing “opt in” and “opt out” options, the presentdisclosure contemplates providing notifications relating to the accessor use of personal information. For instance, a user may be notifiedupon downloading an app that their personal information data will beaccessed and then reminded again just before personal information datais accessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data at a city level rather than at an addresslevel), controlling how data is stored (e.g., aggregating data acrossusers), and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, the likelihoodthat a user utterance is directed to a virtual assistant can beestimated based on non-personal information data or a bare minimumamount of personal information, such as the content being requested bythe device associated with a user, other non-personal informationavailable to the virtual assistant, or publically available information.

What is claimed is:
 1. An electronic device, comprising: one or moreprocessors; a microphone; and memory storing one or more programsconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: receiving, via the microphone, afirst audio stream including one or more utterances; determining whetherthe first audio stream includes a lexical trigger; in accordance with adetermination that the first audio stream includes the lexical trigger,generating one or more candidate text representations of the one or moreutterances; determining whether at least one candidate textrepresentation of the one or more candidate text representations is tobe disregarded by the virtual assistant based on sensory data obtainedfrom one or more sensors of the electronic device; in accordance with adetermination that at least one candidate text representation is to bedisregarded by the virtual assistant, generating one or more candidateintents based on candidate text representations of the one or morecandidate text representations other than the to be disregarded at leastone candidate text representation; determining whether the one or morecandidate intents include at least one actionable intent; in accordancewith a determination that the one or more candidate intents include atleast one actionable intent, executing the at least one actionableintent; outputting a result of the execution of the at least oneactionable intent.
 2. The electronic device of claim 1, wherein thelexical trigger is a single-word lexical trigger.
 3. The electronicdevice of claim 2, wherein the first audio stream includes a firstutterance, and wherein the single-word lexical trigger is positioned ina portion of the first utterance other than the beginning portion of thefirst utterance.
 4. The electronic device of claim 1, whereindetermining whether the first audio stream includes a lexical triggercomprises: detecting a beginning point of the first audio stream;detecting an end point of the first audio stream; and determiningwhether a lexical trigger is included between the beginning point andthe end point of the first audio stream.
 5. The electronic device ofclaim 4, wherein detecting the beginning point of the first audio streamcomprises: detecting, via the microphone, an absence of voice activitybefore receiving the first audio stream; determining whether the absenceof voice activity before receiving the first audio stream exceeds afirst threshold period of time; and in accordance with a determinationthat the absence of voice activity exceeds the first threshold period oftime, determining the beginning point of the first audio stream based onthe absence of voice activity before receiving the first audio stream.6. The electronic device of claim 4, wherein detecting the end point ofthe first audio stream comprises: detecting, via the microphone, anabsence of voice activity after receiving the one or more utterances ofthe first audio stream; determining whether the absence of voiceactivity after receiving the one or more utterances of the first audiostream exceeds a second threshold period of time; and in accordance witha determination that the absence of voice activity after receiving theone or more utterances of the first audio stream exceeds the secondthreshold period of time, determining the end point of the first audiostream based on the absence of voice activity after receiving the one ormore utterances of the first audio stream.
 7. The electronic device ofclaim 4, wherein detecting the end point of the first audio streamcomprises: obtaining a pre-configured duration that the electronicdevice is configured to receive the first audio stream; and determiningthe end point of the first audio stream based on the detected beginningpoint of the first audio stream and the pre-configured duration.
 8. Theelectronic device of claim 4, wherein detecting the end point of thefirst audio stream comprises: determining a size of an audio filerepresenting the received one or more utterances of the first audiostream; comparing the size of the audio file with a capacity of a bufferstoring the audio file; and determining the end point of the first audiostream based on a result of comparing the size of the audio file withthe capacity of the buffer storing the audio file.
 9. The electronicdevice of claim 1, wherein the one or more utterances of the first audiostream include at least one utterance that is not directed to thevirtual assistant.
 10. The electronic device of claim 1, whereingenerating one or more candidate text representations of the one or moreutterances comprises: performing speech-to-text conversion of each ofthe one or more utterances of the first audio stream to generate the oneor more candidate text representations; and determining confidencelevels corresponding to the one or more candidate text representations.11. The electronic device of claim 1, wherein determining whether the atleast one candidate text representation of the one or more candidatetext representations is to be disregarded by the virtual assistantcomprises: determining whether the at least one candidate textrepresentation includes the lexical trigger; and in accordance with adetermination that the at least one candidate text representation doesnot include the lexical trigger, estimating a likelihood that theutterance corresponding to the at least one candidate textrepresentation is not directed to the virtual assistant; anddetermining, based on the estimated likelihood, whether the at least onecandidate text representation of the one or more candidate textrepresentations is to be disregarded by the virtual assistant.
 12. Theelectronic device of claim 11, wherein estimating the likelihood thatthe utterance corresponding to the at least one candidate textrepresentation is not directed to the virtual assistant comprises:obtaining context information associated with a usage pattern of thevirtual assistant; and estimating, based on the context informationassociated with the usage pattern of the virtual assistant, thelikelihood that the utterance corresponding to the at least onecandidate text representation is not directed to the virtual assistant.13. The electronic device of claim 11, wherein estimating the likelihoodthat the utterance corresponding to the at least one candidate textrepresentation is not directed to the virtual assistant comprises:estimating, based on the sensory data, the likelihood that the utterancecorresponding to the at least one candidate text representation is notdirected to the virtual assistant.
 14. The electronic device of claim 1,wherein generating the one or more candidate intents based on thecandidate text representations of the one or more candidate textrepresentations other than the to be disregarded at least one candidatetext representation comprises: obtaining one or more pre-mitigationintents corresponding to the one or more candidate text representationsof the one or more utterances; and selecting, from the one or morepre-mitigation intents, the one or more candidate intents correspondingto the one or more candidate text representations other than the to bedisregarded at least one candidate text representation.
 15. Theelectronic device of claim 1, wherein determining whether the one ormore candidate intents include at least one actionable intent comprises:determining, for each of the one or more candidate intents, whether atask can be performed; and in accordance with a determination that thetask can be performed, determining that the one or more candidateintents include at least one actionable intent.
 16. The electronicdevice of claim 15, wherein determining whether the task can beperformed comprises: obtaining context information associated with ausage pattern of the virtual assistant; and determining, based on thecontext information associated with the usage pattern of the virtualassistant, whether the task can be performed.
 17. The electronic deviceof claim 15, wherein determining whether the task can be performedcomprises: obtaining context information associated with a previous taskperformed by the virtual assistant; and determining, based on thecontext information associated with the previous task performed by thevirtual assistant, whether the task can be performed.
 18. The electronicdevice of claim 15, wherein determining whether the task can beperformed comprises: determining one or more relations among the one ormore candidate text representations; determining whether the task can beperformed based on the one or more relations among the one or morecandidate text representations.
 19. The electronic device of claim 15,wherein determining whether the task can be performed comprises:obtaining sensory data from one or more sensors communicatively coupledto the electronic device; and determining whether the task can beperformed based on the sensory data.
 20. The electronic device of claim15, wherein determining whether the task can be performed comprises:estimating a confidence level associated with performing the task;determining whether the confidence level associated with performing thetask satisfies a threshold confidence level; and in accordance with adetermination that the confidence level associated with performing thetask satisfies the threshold confidence level, determining that the taskcan be performed.
 21. The electronic device of claim 1, whereinexecuting the at least one actionable intent comprises: performing oneor more tasks according to the at least one actionable intent.
 22. Theelectronic device of claim 1, wherein the one or more candidate intentsincludes a plurality of actionable intents, and where executing the atleast one actionable intent comprises: selecting, from a plurality oftasks associated with the plurality of actionable intents, a first taskfor execution; and performing the selected first task.
 23. Theelectronic device of claim 22, wherein selecting, from the plurality oftasks associated with the plurality of actionable intents, the firsttask for execution comprises: obtaining context information associatedwith a most-recent task initiated by the virtual assistant; andselecting the first task based on the context information associatedwith a previous task performed by the virtual assistant.
 24. Theelectronic device of claim 22, wherein selecting, from the plurality oftasks associated with the plurality of actionable intents, the firsttask for execution comprises: outputting a plurality of task optionscorresponding to the plurality of tasks associated with the plurality ofactionable intents; receiving a user selection from the plurality oftask options; and selecting the first task based on the user selection.25. The electronic device of claim 22, wherein selecting, from theplurality of tasks associated with the plurality of actionable intents,the first task for execution comprises: determining a priorityassociated with each of the plurality of tasks; and selecting the firsttask for execution based on the priority associated with each of theplurality of tasks.
 26. The electronic device of claim 1, wherein theone or more programs comprise further instructions for: upon executingthe at least one actionable intent, receiving, via the microphone, asecond audio stream; generating one or more second candidate textrepresentations to represent the second audio stream; determining, basedon the one or more second candidate text representations, whether thesecond audio stream is a part of an audio session that includes thefirst audio stream; in accordance with a determination that the secondaudio stream is a part of the audio session that includes the firstaudio stream, generating, based on the one or more second candidate textrepresentations, one or more second candidate intents; determiningwhether the one or more second candidate intents include at least onesecond actionable intent; in accordance with a determination that theone or more second candidate intents include at least one secondactionable intent, executing the at least one second actionable intent;and outputting a result of the execution of the at least one secondactionable intent.
 27. The electronic device of claim 26, whereindetermining whether the second audio stream is a part of the audiosession that includes the first audio stream comprises: obtainingcontext information associated with executing the at least oneactionable intent; and determining, based on the context informationassociated with executing the at least one actionable intent, whetherthe second audio stream is a part of the audio session that includes thefirst audio stream.
 28. The electronic device of claim 26, whereindetermining whether the second audio stream is a part of the audiosession that includes the first audio stream comprises: determining arelation among respective candidate text representations of the firstaudio stream and the second audio stream; and determining, based on therelation among respective candidate text representations of the firstaudio stream and the second audio stream, whether the second audiostream is a part of the audio session that includes the first audiostream.
 29. A method for providing natural language interaction by avirtual assistant, the method comprising: at an electronic device withone or more processors, memory, and a microphone: receiving, via amicrophone, a first audio stream including one or more utterances;determining whether the first audio stream includes a lexical trigger;in accordance with a determination that the first audio stream includesthe lexical trigger, generating one or more candidate textrepresentations of the one or more utterances; determining whether atleast one candidate text representation of the one or more candidatetext representations is to be disregarded by the virtual assistant basedon sensory data obtained from one or more sensors of the electronicdevice; in accordance with a determination that at least one candidatetext representation is to be disregarded by the virtual assistant,generating one or more candidate intents based on candidate textrepresentations of the one or more candidate text representations otherthan the to be disregarded at least one candidate text representation,wherein generating the one or more candidate intents comprises;determining whether the one or more candidate intents include at leastone actionable intent; in accordance with a determination that the oneor more candidate intents include at least one actionable intent,executing the at least one actionable intent; outputting a result of theexecution of the at least one actionable intent.
 30. A non-transitorycomputer-readable storage medium storing one or more programs configuredto be executed by one or more processors of an electronic device, theone or more programs including instructions for: receiving, via amicrophone, a first audio stream including one or more utterances;determining whether the first audio stream includes a lexical trigger;in accordance with a determination that the first audio stream includesthe lexical trigger, generating one or more candidate textrepresentations of the one or more utterances; determining whether atleast one candidate text representation of the one or more candidatetext representations is to be disregarded by the virtual assistant basedon sensory data obtained from one or more sensors of the electronicdevice; in accordance with a determination that at least one candidatetext representation is to be disregarded by the virtual assistant,generating one or more candidate intents based on candidate textrepresentations of the one or more candidate text representations otherthan the to be disregarded at least one candidate text representation;determining whether the one or more candidate intents include at leastone actionable intent; in accordance with a determination that the oneor more candidate intents include at least one actionable intent,executing the at least one actionable intent; outputting a result of theexecution of the at least one actionable intent.